Water Scarcity Management: Towards the Application of Artificial Intelligence and Earth Observation Data (Advanced Topics in Biomaterials)

Drought and the condition of water scarcity lead to several socio-economic, social and environmental impacts. Whatever the approaches of drought management, policymakers and planners require novel methods to analyze data and model drought processes and its connection with water scarcity. In recent years, artificial intelligence-based and earth observation approaches have been progressively developed and applied in domain of water-related disasters. The target of this book is to present new advances and achievements in the fields of drought monitoring, analyzing, and modeling using artificial intelligence algorithms (e.g., machine learning, deep learning, etc.), statistical indices, and a diverse range of satellite remote sensing and geo-spatial data sets. Water Scarcity Management: Towards the Application of Artificial Intelligence and Earth Observation Data will help students gain knowledge on drought prediction using new free-access earth observation data and machine learning models. It will also guide scientists, researchers, and urban planners with the monitoring of water resources and key elements of hydrological cycle.

• Covers application of a range of diverse artificial intelligence algorithms for spatial modeling of drought susceptibility
• Includes real case studies from large geographical areas
• Offers earth observation satellite data for spatio-temporal analysis of drought and water scarcity
• Combines geo-environmental and topo-hydrological factors with drought and water scarcity issues

Dr. Omid Rahmati is a geo-environmental researcher and Assistant Professor at the Agricultural Research, Education, and Extension Organization (AREEO) in Iran. His research focuses on applying machine learning models to natural hazard mitigation and watershed management. He has authored and co-authored over 70 articles in international peer-reviewed journals, as well as several books and book chapters. Dr. Rahmati’s publications have been cited more than 12,500 times (H-index: 56), and he has been recognized as a Highly Cited Researcher. He is ranked among the World’s Top 1% of Scientists by Web of Science (Clarivate, 2021–2022) and listed in Stanford University’s “World’s Top 2% Scientists” from 2021 to 2025. His publications over the past decade reflect a broad and significant influence in his field.

Dr. Assefa M. Melesse is a Distinguished University Professor of Water Resources Engineering at Florida International University. He earned his ME (2000) and PhD (2002) from the University of Florida in Agricultural Engineering. His areas of research and experience include climate change impact modeling, watershed modeling, ecohydrology, sediment transport, surface and groundwater interactions modeling, water–energy–carbon fluxes coupling and simulations, remote sensing hydrology, river basin management, and land cover change detection and scaling. Dr. Melesse is a registered Professional Engineer (PE), Board Certified Enviromental Engineer (BCEE) and also a Board Certified Water Resources Engineer (BC.WRE) with over 30 years of teaching and research experience, and has authored/edited 11 books, over 230 journal articles, and over 100 book chapters.

Dr. Amir Naghibi is an Assistant Professor in the Division of Water Resources Engineering and the Center for Advanced Middle Eastern Studies at Lund University, Lund, Sweden. His work sits at the intersection of artificial intelligence (AI), computer science, remote sensing, and environmental systems modeling, addressing the water-climate-food-energy nexus. He develops advanced AI-driven decision support systems to address complex challenges in water resources, agriculture, climate adaptation, and natural hazard management. He has published more than 50 papers in peer-reviewed journals with over 6,000 citations and has been listed in Stanford University’s World’s Top 2% Scientists from 2020 to 2025, demonstrating his broad and significant scientific influence.